Energy consumption modeling

ABSTRACT

Methods, devices, and systems for energy consumption modeling are described herein. One device includes a memory, and a processor configured to execute executable instructions stored in the memory to assign energy consumption data to an on state and an off state of a device, generate a model configured to predict a future state of the device and a duration of the future state based on a duration of a current state of the device, a duration of a previous state of the device, and operating conditions of the device, and predict a future energy consumption of the device using the assigned energy consumption data and the generated model.

TECHNICAL FIELD

The present disclosure relates to methods, devices, and systems forenergy consumption modeling.

BACKGROUND

Connected devices can enable users to wirelessly access and controlthose devices. For example, a connected thermostat can allow a user tochange the temperature settings of their home from any location.

Connected devices have been gaining popularity among consumers. Forexample, many commercial buildings utilize smart automation and controlsystems for building system control. In particular, many commercialbuildings can utilize smart automation and controls to operate heating,ventilation, and air-conditioning (HVAC) systems based on a number ofparameters (e.g., time of day, day of the week, seasonal ambienttemperatures, etc.) This data can be useful for utility companies forload forecasting and estimating real-time demand response capacity.

However, this information can be more difficult for utility companies toacquire in the residential sector. The difficulty in obtaining thisinformation can result from simple devices typically found in manyresidences that can include only ON/OFF states, or devices with only afew ON states as well as an OFF state. Further, many of these simpledevices are not connected devices. Residential users may not want topurchase more complex and connected control systems as cost can be aprohibitive factor.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a histogram of logged energyconsumption data of a device that can be utilized in one or moreembodiments of the present disclosure.

FIG. 2 illustrates an example of energy consumption data history thatcan be utilized in one or more embodiments of the present disclosure.

FIG. 3 illustrates an example of predicted durations of states that canbe utilized in one or more embodiments of the present disclosure.

FIG. 4 illustrates an example comparison of predicted to measured statesthat can be utilized in one or more embodiments of the presentdisclosure.

FIG. 5 is a schematic block diagram of a controller for energyconsumption modeling, in accordance with one or more embodiments of thepresent disclosure.

DETAILED DESCRIPTION

Methods, devices, and systems for energy consumption modeling aredescribed herein. For example, one or more embodiments include a memory,and a processor configured to execute executable instructions stored inthe memory to assign energy consumption data to an on state and an offstate of a device, generate a model configured to predict a future stateof the device and a duration of the future state based on a duration ofa current state of the device, a duration of a previous state of thedevice, and operating conditions of the device, and predict a futureenergy consumption of the device using the assigned energy consumptiondata and the generated model.

Energy consumption modeling, in accordance with the present disclosure,can allow a utility to enable services such as load forecasting forconsumption aggregators and/or decision support for residential demandresponse by utilizing consumption data from each individual connecteddevice among a population of connected devices. These services can beimplemented by more easily capturing energy consumption data that isavailable from residential users who use connected devices. Further,energy consumption modeling in accordance with the present disclosurecan provide a financial incentive to encourage residential users topurchase a connected device by monitoring for performance degradationand/or detecting faults in a connected device.

In the following detailed description, reference is made to theaccompanying drawings that form a part hereof. The drawings show by wayof illustration how one or more embodiments of the disclosure may bepracticed.

These embodiments are described in sufficient detail to enable those ofordinary skill in the art to practice one or more embodiments of thisdisclosure. It is to be understood that other embodiments may beutilized and that process, electrical, and/or structural changes may bemade without departing from the scope of the present disclosure.

As will be appreciated, elements shown in the various embodiments hereincan be added, exchanged, combined, and/or eliminated so as to provide anumber of additional embodiments of the present disclosure. Theproportion and the relative scale of the elements provided in thefigures are intended to illustrate the embodiments of the presentdisclosure, and should not be taken in a limiting sense.

The figures herein follow a numbering convention in which the firstdigit or digits correspond to the drawing figure number and theremaining digits identify an element or component in the drawing.

As used herein, “a” or “a number of” something can refer to one or moresuch things. For example, “a number of previous states” can refer to oneor more previous states.

FIG. 1 illustrates an example of a histogram 100 of logged energyconsumption data 102 of a device that can be utilized in one or moreembodiments of the present disclosure. As shown in FIG. 1, the histogram100 can include logged energy consumption data 102-1, 102-2, and 102-3.

Energy consumption data can include data corresponding to theconsumption of electrical energy by an electrical device. For example, adevice (e.g., a refrigerator) can consume an amount of electrical energy(e.g., 100 Watts) over a period of time (e.g., 12 hours).

Energy consumption data 102-1, 102-2, and 102-3 can be assigned by acontroller (e.g., controller 540 as described in connection with FIG. 5)to an off state and an on state of a device. For example, energyconsumption data 102-1 can be assigned to an off state, energyconsumption data 102-2 can be assigned to a first on state, and energyconsumption data 102-3 can be assigned to a second on state, as will befurther described herein.

In some embodiments, the device can be a thermostatically controlleddevice. A thermostatically controlled device, as used herein, can be adevice that automatically responds to changes in temperature. Forexample, a thermostatically controlled device can include a thermostat,an air-conditioner, a heat pump, a boiler, a refrigerator, a freezer, orother residential appliances. However, embodiments of the presentdisclosure are not so limited.

In some embodiments, the controller can be part of a residentialthermostat. For example, a residential thermostat can include acontroller to assign energy consumption data to each state of thethermostat.

In some embodiments, the controller can be part of an energy meter. Forexample, the energy meter can be a device that measures the amount ofelectric energy consumed by a residence, business, or an electricallypowered device. That is, the energy meter can be a meter measuringenergy consumption for an entire residence or business, or a meterdedicated to a single electrically powered device.

In some embodiments, the controller can be remote to the device and/orthe energy meter. The controller can be connected to the device and/orthe energy meter via a network. As used herein, a network can be anetwork relationship that connects the controller to the device and/orthe energy meter. For example, the controller can be connected to thedevice and/or the energy meter via a local area network (LAN), wide areanetwork (WAN), personal area network (PAN), a distributed computingenvironment (e.g., a cloud computing environment), and/or the Internet,among other types of network relationships.

In the example shown in FIG. 1, the device can have multiple states. Forexample, the device can have an off state, a first on state, and asecond on state. As an example, an air-conditioner can have an off stateand a plurality of on states. The plurality of on states can correspondto the levels of cooling capacity (e.g., ability to remove heat)associated with the air-conditioner. That is, a first on state cancorrespond to a cooling capacity that is lower relative to a coolingcapacity associated with a second on state.

Although the device is described as consisting of three states,embodiments of the present disclosure are not so limited. For example,the amount of states can vary by device. That is, a device can consistof two states (e.g., an off state and an on state), or more than threestates (e.g., an off state, and N number of on states).

Each of the plurality of states of a device can be associated with adifferent level of energy consumption. For example, the plurality of onstates of an air conditioner can correspond to different levels ofcooling capacity, and therefore different levels of energy consumption.As the cooling capacity differs at each on state (e.g., the first onstate can remove 10 Watts of heat energy, the second on state can remove20 Watts of heat energy), the energy consumption of each state can alsodiffer (e.g., first on state consumes 10 Watts of electricity, second onstate consumes 20 Watts of electricity).

The controller can receive the energy consumption data from an energymeter connected to the device. For example, the controller can receiveand log energy consumption data 102-1, 102-2, and 102-3 measured by anenergy meter.

The controller can assign energy consumption data 102-1, 102-2, and102-3 to a plurality of states of the device. For example, energyconsumption can depend primarily on the state of the device. Thecontroller can process energy consumption data 102-1 received from theenergy meter and assign an off state to energy consumption data 102-1based on the level of power of energy consumption data 102-1 (e.g.,based on the low power level of energy consumption data 102-1 ascompared to energy consumption data 102-2 and 102-3). Further, thecontroller can assign a first on state to energy consumption data 102-2,and a second on state to energy consumption data 102-3.

In some embodiments, the controller can assign energy consumption data102-1, 102-2, and 102-3 to a device state based on peak (e.g., mostfrequent) values of logged consumption data. For example, the controllercan process energy consumption data 102-1, 102-2, and 102-3, thatresults in a peak value of 10 Watts that was logged during a first timeperiod, a peak value of 700 Watts that was logged during a second timeperiod, and a peak value of 950 Watts that was logged during a thirdtime period. The controller can assign, based on the peak values, energyconsumption data 102-1, 102-2, and 102-3 to a plurality of states of thedevice. That is, the controller can assign energy consumption data 102-1to an off state, energy consumption data 102-2 to a first on state, andenergy consumption data 102-3 to a second on state based on the peakvalues of 10 Watts, 700 Watts, and 950 Watts, respectively.

In some embodiments, the controller can assign energy consumption databased on a distribution of the histogram 100. The controller can processenergy consumption data 102-1, 102-2, and 102-3 that results in adistribution of data for energy consumption data 102-2 and 102-3. Thecontroller can assign energy consumption data 102-2 and 102-3 to a firston state and a second on state, respectively, based on the distributionof energy consumption data 102-2 and 102-3 on histogram 100. Forexample, the controller can assign energy consumption data 102-2 and102-3 using a Gaussian mixture model. However, embodiments of thepresent disclosure are not so limited. For example, the controller canassign energy consumption data 102-2 and 102-3 using any otherstatistical technique utilizing the distribution of the data inhistogram 100.

Although energy consumption data 102-1, 102-2, and 102-3 are describedas being logged by an energy meter, embodiments of the presentdisclosure are not so limited. For example, an energy meter may not beavailable to log energy consumption data 102-1, 102-2, and 102-3 of thedevice. In some such embodiments, the energy consumption data 102-1,102-2, and 102-3 can be nominal energy consumption data. For example,nominal energy consumption data can be energy consumption data from adatasheet of the device (e.g., energy consumption data provided by thedevice manufacturer). The datasheet of the device can provide projectedenergy consumption data of each stage of the device.

Nominal energy consumption data can be entered into the controllermanually. For example, nominal energy consumption data can be entered bya user (e.g., a building manager, contractor, technician, etc.) duringinstallation of the device. That is, energy consumption data 102-1,102-2, and 102-3 can be data manually entered into the controller from adevice datasheet during installation of the device by a contractor orother user.

In some embodiments in which an energy meter is not available, theenergy consumption data can be received by the controller fromadditional users. Additional users can have similar and/or the samedevices with similar and/or the same device consumption data. Forexample, the controller can receive the similar energy consumption datafrom additional users that have similar device consumption data via anetwork relationship such as a local area network (LAN), wide areanetwork (WAN), personal area network (PAN), a distributed computingenvironment (e.g., a cloud computing environment), and/or the Internet,among other types of network relationships.

Similarity of data can include whether the additional users have thesame devices (e.g., same type, same manufacturer), or a similar device(e.g., in the case of an air-conditioner, similar cooling capacity,similar number of states, similarity of compressor, etc.) Further,similarity of data can include operating conditions (e.g., geographicallocation, average ambient temperatures, daily temperature ranges, etc.)

FIG. 2 illustrates an example of energy consumption data history 210that can be utilized in one or more embodiments of the presentdisclosure. As shown in FIG. 2, the energy consumption data history 210can include off state data 212, on state data 216, off state outlierdata 214, and on state outlier data 218.

A controller (e.g., controller 540, as described in connection with FIG.5) can generate a model configured to predict a future state of a deviceand a duration of the future state of the device based on a duration ofa current state of the device, a duration of a previous state of thedevice, and operating conditions of the device. The operating conditionsof the device can include, for example, the ambient outside airtemperature, a temperature setpoint of the device, and/or a combinationthereof.

Although operating conditions of the device are described as includingambient outside air temperature and/or a temperature setpoint of thedevice, embodiments of the present disclosure are not so limited. Forexample, operating conditions of the device can include other variablesto predict a future state of the device.

The model can be generated by, for example, generating a probabilitydistribution for a duration of the current state of the device as afunction of the duration of the previous state of the device, andpredicting a transition between the current state of the device and thefuture state of the device using the probability distribution and anamount of time already spent in the current state, as will be furtherdescribed herein (e.g., in connection with FIG. 3).

The model can be in the form of a probabilistic state machine. As shownin FIG. 2, energy consumption data history 210 can include on state data216 and off state data 212. The on state data 216 and off state data 212can be data logged by the controller (e.g., as described in connectionwith FIG. 1) or, in the case of an energy meter not being available, canbe nominal consumption data inferred from a datasheet of the device(e.g., as described in connection with FIG. 1), or energy consumptiondata received from additional users (e.g., as described in connectionwith FIG. 1).

In the embodiment shown in FIG. 2, the current state of the device isoff, as represented by the vertical axis of FIG. 2. Additionally, theprevious state of the device is on, as represented by the horizontalaxis of FIG. 2. For example, a prediction as to the next state (e.g.,the on state) and the duration of the next state (e.g., the on state)can be made based on the duration of the current state (e.g., the offstate) and a duration of the previous state (e.g., the on state).

The probability distribution for the duration of the current state(e.g., the off state) of the device as a function of the duration of theprevious state (e.g., the on state) of the device can be derived fromenergy consumption data history 210 of off state data 212 and on statedata 216. For example, the probability distribution of the duration ofthe off state (e.g., the current state) can be derived based on the timealready spent in the off state as a function of the length of the onstate (e.g., the previous state).

The model can be updated based on additional energy consumption datareceived by the controller from an energy meter. For example, with everynew state transition (e.g., the off state to the on state, and/or the onstate to the off state) that is logged and detected, the statistics ofthe state transitions can be updated. That is, new data can be added toenergy consumption data history 210 by adding new off state data 212 andnew on state data 216 as it is received by the controller.

As the model is updated based on additional energy consumption data, theprobability distribution for the duration of the current state of thedevice as a function of the duration of the previous state of the devicecan also be updated. For example, the probability of the duration of thecurrent state can continuously be updated for accuracy based onadditional energy consumption data. Continuously updating theprobability distribution in this way can maintain accuracy of the model.

The update frequency of the model can vary. For example, the frequencywith which the model is updated can be selected by a user. The user canset the update frequency as a particular number of state transitions(e.g., 10 state transitions before the model is updated). As anotherexample, the user can set the update frequency as a particular timeperiod (e.g., set the model to update once per hour, once per day, etc.)Additionally, the model can be updated monthly and/or seasonally (e.g.,fall, winter, spring, and/or summer).

The probability distribution can be generated while ignoring outlierdata. Outlier data, as used herein, can be data that, due to variabilityin a measurement or some other error, is too distant from other data tobe used in the probability distribution.

For example, as shown in FIG. 2, energy consumption data history 210 caninclude off state outlier data 214 and on state outlier data 218. Offstate outlier data 214 and on state outlier data 218 can be excludedfrom use in off state data 212 and on state data 216, respectively, asinclusion of outlier data can yield unexpected and erroneous probabilitydistributions.

Off state outlier data 214 and on state outlier data 218 can beidentified and excluded using various statistical techniques. Forexample, outlier data can be excluded using trimmed means, where aselected percentage of the largest and smallest data observations areremoved from the sample population. However, embodiments of the presentdisclosure are not so limited. For example, any other statisticaltechnique to identify and exclude outliers may be used.

Although the model is described as comprising only an off state and anon state, embodiments of the present disclosure are not so limited. Forexample, the controller can generate a model with an off state and aplurality of on states.

The controller can generate a model configured to predict a duration ofa plurality of future states of the device based on the duration of thecurrent state of the device, a duration of the plurality of previousstates of the device, and operating conditions of the device. That is,the controller can generate a probability distribution for a duration ofa current state of the plurality of on states of the device as afunction of the number of the plurality of on states.

Analogous to the embodiment with an off state and an on state, the modelcomprising a plurality of on states can be in the form of aprobabilistic state machine, with off state data and a plurality of onstate data logged by the controller or received by the controllerthrough nominal data or from other users. The probability distributionfor the duration of the current on state of the device as a function theduration of the previous on state of the device can be derived fromenergy consumption data history.

The plurality of predicted durations of the future states of the devicecan be the same as the plurality of durations of the previous states ofthe device. That is, the probability distribution of the duration of thecurrent on state can be derived based on the time already spent in theon state as a function of the length of the number of previous onstates. For example, if the device comprises three on states, theprobability distribution can utilize the length of the previous three onstates in determining the distribution of the duration of the current onstate and/or the predicted duration of the next three on states.

FIG. 3 illustrates an example of predicted durations of states 320 thatcan be utilized in one or more embodiments of the present disclosure. Asshown in FIG. 3, the predicted durations of states 320 can includepredicted durations of a current state 322, a future state 324, and anadditional future state 326.

A controller (e.g., controller 540, as described in connection with FIG.5) can generate a model configured to predict a future state of a deviceand a duration of the future state of the device based on a duration ofa current state of the device, a duration of a previous state of thedevice, and operating conditions of the device. The model can begenerated by generating a probability distribution for a duration of thecurrent state of the device as a function of the duration of theprevious state of the device (e.g., as described previously inconnection with FIG. 2), and predicting a transition between the currentstate of the device and the future state of the device using theprobability distribution and an amount of time already spent in thecurrent state.

For example, the controller can predict the start of future state 324based on an amount of time already spent in current state 322. Thechange from current state 322 to future state 324 can be predicted usingthe most likely switching time from the probability distribution (e.g.,probability distribution as described in connection with FIG. 2),decreased by the time already spent in current state 322.

Although described as using the most likely switching time from theprobability distribution as the property predicting a transition betweenstates, embodiments of the present disclosure are not so limited. Forexample, the whole distribution can be used and a suitable value such asa maximum likelihood may be selected as the property to predict atransition between current state 322 and future state 324.

The prediction of a transition between states can be replicatedinfinitely. For example, the prediction can be utilized to predict afuture state 324, and an additional future state 326. Additionally, theprediction can be used to predict further additional future states.

Prediction of additional future state 326 can include utilizing datafrom a number of previous states. For example, additional future state326 can be two states in the future from the current state 322. Thecontroller can utilize two previous states from current state 322 topredict additional future state 326. That is, the prediction of thenumber of future states can be based on a number of previous statesequal to the number of predicted future states.

The controller can predict a future energy consumption of the deviceusing the assigned energy consumption data (e.g., energy consumptiondata previously described in connection with FIG. 1) and the generatedmodel (e.g., the generated model previously described in connection withFIG. 2). For example, the controller can utilize previous energyconsumption data along with predicted transitions between states togenerate load forecasting. That is, the controller can generateestimates of potential load demand for the device, as will be furtherdescribed herein.

Although shown in FIG. 3 as a device comprising only an off state and anon state, embodiments of the present disclosure are not so limited. Forexample, the controller can predict a transition between each of aplurality of on states of a device using the probability distributionand an amount of time already spent by the device in the current onstate using the techniques (e.g., most likely switching time from theprobability distribution, using the entire distribution, etc.)previously described. Further, similar techniques can be utilized topredict future energy consumption of the device with an off state and aplurality of on states.

Generating estimates of potential load demand for the device can beuseful for utility companies. For example, a utility company can utilizethe potential load estimates to determine demand response capacity. Thatis, a utility company can compile data from a number of controllersconnected to a number of residences with a number of devices to predictwhen an electrical demand might be high, and provide an adequateresponse to the rise in electrical demand.

FIG. 4 illustrates an example comparison of predicted to measured states428 that can be utilized in one or more embodiments of the presentdisclosure. As shown in FIG. 2, the example comparison 428 can includeassigned energy consumption data 430 and predicted energy consumptiondata 432.

A controller (e.g., controller 540, as described in connection with FIG.5) can detect a fault of a device using the assigned energy consumptiondata 430 and the generated model (e.g., the model configured to predicta future state of a device and a duration of the future state of thedevice, as described in connection with FIG. 2). The generated model caninclude predicted energy consumption data 432.

Detecting a fault of the device can include comparing the assignedenergy consumption data 430 to the predicted future energy consumptiondata 432. For example, as shown in FIG. 4, a first comparison 434between assigned energy consumption data 430 and predicted energyconsumption data 432 can be made. At first comparison 434, assignedenergy consumption data 430 and predicted energy consumption data 432appear to be in step without any drift, indicating that there is notcurrently any faults with the device.

A second comparison 436 can be made between assigned energy consumptiondata 430 and predicted energy consumption data 432. At second comparison436, assigned energy consumption data 430 appears to drift frompredicted energy consumption data 432. The drift of assigned energyconsumption data 430 can indicate degradation of the device. The devicemay be using more energy than previous instances of on states,indicating that the device may need to be repaired due to aninefficiency in operation. For example, a worn part of the device may becausing the device to consume more energy than needed, resulting in thedevice consuming more energy.

A third comparison 438 can be made between assigned energy consumptiondata 430 and predicted energy consumption data 432. At third comparison438, a step change of assigned energy consumption data 430 appears tooccur from predicted energy consumption data 432. The step change ofassigned energy consumption data 432 can indicate a fault of the device.For example, the device may be faulty in some regard and may need to bereplaced.

Fault detection by the controller can provide financial benefits toresidential users. For example, notification of a drift (e.g., a driftas shown in second comparison 436) of assigned energy consumption data430 from predicted energy consumption data 432 to a residential user canallow the user to promptly replace a part that is causing a device tooperate inefficiently relative to a normal operating state. Further, astep change (e.g., a step change as shown in third comparison 438) ofassigned energy consumption data 430 from predicted energy consumptiondata 432 can lead to a user being notified of the fault and to replacethe device, if needed.

FIG. 5 is a schematic block diagram of a controller 540 for energyconsumption modeling, in accordance with one or more embodiments of thepresent disclosure. Controller 540 can be, for example, controllerspreviously described in connection with FIGS. 1-4. For example,controller 540 can include a memory 544 and a processor 542 configuredfor energy consumption modeling in accordance with the presentdisclosure.

The memory 544 can be any type of storage medium that can be accessed bythe processor 542 to perform various examples of the present disclosure.For example, the memory 544 can be a non-transitory computer readablemedium having computer readable instructions (e.g., computer programinstructions) stored thereon that are executable by the processor 542 toassign energy consumption data to an on state and an off state of adevice and generate a model configured to predict a future state of thedevice and a duration of the future state of the device based on aduration of a current state of the device, a duration of a previousstate of the device, and operating conditions of the device. Further,processor 542 can execute the executable instructions stored in memory544 to predict future energy consumption of the device using theassigned energy consumption data and the generated model.

The memory 544 can be volatile or nonvolatile memory. The memory 544 canalso be removable (e.g., portable) memory, or non-removable (e.g.,internal) memory. For example, the memory 544 can be random accessmemory (RAM) (e.g., dynamic random access memory (DRAM) and/or phasechange random access memory (PCRAM)), read-only memory (ROM) (e.g.,electrically erasable programmable read-only memory (EEPROM) and/orcompact-disc read-only memory (CD-ROM)), flash memory, a laser disc, adigital versatile disc (DVD) or other optical storage, and/or a magneticmedium such as magnetic cassettes, tapes, or disks, among other types ofmemory.

Further, although memory 544 is illustrated as being located withincontroller 540, embodiments of the present disclosure are not solimited. For example, memory 544 can also be located internal to anothercomputing resource (e.g., enabling computer readable instructions to bedownloaded over the Internet or another wired or wireless connection).

As used herein, “logic” is an alternative or additional processingresource to execute the actions and/or functions, etc., describedherein, which includes hardware (e.g., various forms of transistorlogic, application specific integrated circuits (ASICs), etc.), asopposed to computer executable instructions (e.g., software, firmware,etc.) stored in memory and executable by a processor. It is presumedthat logic similarly executes instructions for purposes of theembodiments of the present disclosure.

Although specific embodiments have been illustrated and describedherein, those of ordinary skill in the art will appreciate that anyarrangement calculated to achieve the same techniques can be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments of thedisclosure.

It is to be understood that the above description has been made in anillustrative fashion, and not a restrictive one. Combination of theabove embodiments, and other embodiments not specifically describedherein will be apparent to those of skill in the art upon reviewing theabove description.

The scope of the various embodiments of the disclosure includes anyother applications in which the above structures and methods are used.Therefore, the scope of various embodiments of the disclosure should bedetermined with reference to the appended claims, along with the fullrange of equivalents to which such claims are entitled.

In the foregoing Detailed Description, various features are groupedtogether in example embodiments illustrated in the figures for thepurpose of streamlining the disclosure. This method of disclosure is notto be interpreted as reflecting an intention that the embodiments of thedisclosure require more features than are expressly recited in eachclaim.

Rather, as the following claims reflect, inventive subject matter liesin less than all features of a single disclosed embodiment. Thus, thefollowing claims are hereby incorporated into the Detailed Description,with each claim standing on its own as a separate embodiment.

What is claimed:
 1. A controller for an energy consumption modelingsystem, comprising: a memory; and a processor configured to executeexecutable instructions stored in the memory to: assign energyconsumption data to an on state and an off state of a device; generate amodel configured to predict a future state of the device and a durationof the future state of the device by: generating a probabilitydistribution for a duration of a current state of the device as afunction of a duration of a previous state of the device and operatingconditions of the device that include a temperature setpoint of thedevice and a geographic location of the device; and predicting atransition between the current state of the device and the future stateof the device using the probability distribution and an amount of timealready spent by the device in the current state; predict a futureenergy consumption of the device using the assigned energy consumptiondata and the generated model, wherein the predicted future energyconsumption of the device is used by a utility to perform a demandresponse event; and detect a fault of the device using the assignedenergy consumption data and the generated model.
 2. The controller ofclaim 1, wherein the controller is part of a residential thermostat. 3.The controller of claim 1, wherein the device is a thermostaticallycontrolled device.
 4. The controller of claim 1, wherein the controlleris part of an energy meter.
 5. The controller of claim 1, whereinprocessor is configured to execute the instructions to receive theenergy consumption data from an energy meter connected to the device. 6.The controller of claim 1, wherein the energy consumption data of thedevice is nominal energy consumption data.
 7. A method for operating anenergy consumption modeling system, comprising: assigning, by acontroller, energy consumption data to a plurality of states of adevice, wherein each of the plurality of states is associated with adifferent level of energy consumption; generating, by the controller, amodel configured to predict a future state of the device and a durationof a future state of the device by: generating a probabilitydistribution for a duration of a current state of the device as afunction of a duration of a previous state of the device and operatingconditions of the device that include a temperature setpoint of thedevice and a geographic location of the device; and predicting atransition between the current state of the device and the future stateof the device using the probability distribution and an amount of timealready spent by the device in the current state; predicting, by thecontroller, a future energy consumption of the device using the assignedenergy consumption data and the generated model; detecting, by thecontroller, a fault of the device using the assigned energy consumptiondata and the generated model; and performing, by a utility, a demandresponse event based on the predicted future energy consumption of thedevice.
 8. The method of claim 7, wherein the method includes receivingthe energy consumption data from additional users.
 9. The method ofclaim 7, wherein the method includes updating the model based onadditional energy consumption data received from an energy meter. 10.The method of claim 9, wherein method includes updating the probabilitydistribution based on the additional energy consumption data.
 11. Themethod of claim 7, wherein the method includes generating theprobability distribution while ignoring outlier data.
 12. The method ofclaim 7, wherein the method further includes predicting future energyconsumption of a device with an off state and a plurality of on states.13. The method of claim 12, wherein predicting future energy consumptiondata of the device with an off state and a plurality of on statesincludes: generating a probability distribution for a duration of acurrent state of the plurality of on states of the device as a functionof the number of the plurality of on states and operating conditions ofthe device; predicting a transition between each of the plurality of onstates of the device using the probability distribution and an amount oftime already spent by the device in the current on state.
 14. Acontroller for an energy consumption modeling system, comprising: amemory; and a processor configured to execute executable instructionsstored in the memory to: assign energy consumption data to an off stateand a plurality of on states of a device; generate a model configured topredict a future state of the device and a duration of a future state ofthe device by: generating a probability distribution for a duration of acurrent state of the device as a function of a duration of a previousstate of the device and operating conditions of the device that includea temperature setpoint of the device and a geographic location of thedevice; and predicting a transition between the current state of thedevice and the future state of the device using the probabilitydistribution and an amount of time already spent by the device in thecurrent state; predict a future energy consumption of the device usingthe assigned energy consumption data and the generated model, whereinthe predicted future energy consumption of the device is used by autility to perform a demand response event; and detect a fault of thedevice using the assigned energy consumption data and the generatedmodel.
 15. The method of claim 14, wherein detecting the fault of thedevice includes comparing the assigned energy consumption data of thedevice to the predicted future energy consumption data.
 16. Thecontroller of claim 14, wherein each of the plurality of on states ofthe device is associated with a different level of energy consumption.17. The controller of claim 14, wherein the controller is part of acloud computing environment.
 18. The controller of claim 14, wherein theprocessor is configured to execute the instructions to generate a modelconfigured to predict a duration of a plurality of future states of thedevice based on the duration of the current state of the device, aduration of a plurality of previous states of the device, and theoperating conditions of the device.
 19. The controller of claim 14,wherein the operating conditions of the device include an ambienttemperature.